Abstract
A neural-network inverse mapping approach was used in a structural integrity redesign problem to achieve the desired strength, corrosion, and fatigue properties. Also, through the inverse-mapping procedure the relative importance of damage parameters was obtained for two trained neural-network models. The damage parameters corresponding to a given strength and corrosion rate are predicted in one network, whereas the damage parameters corresponding to a given corrosion fatigue life are predicted in the other network. The results obtained from the inverse mapping procedure are compared with actual panel configurations and environments and experimental data. The results obtained through the inverse-mapping procedure are found to agree well with the experimental data, thus demonstrating the feasibility of the approach for the structural integrity redesign of aging aircraft structures.
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